Highly Modular Architecture for the General Control of Autonomous Robots

The implementation in a robot of the coordination between different sensors and actuators in order to achieve a task requires a high formulation and modelisation effort, specially when the number of sensors/actuators and degrees of freedom available in the robot is huge. This paper introduces a highly distributed architecture that is independent from the robot platform, capable of the generation of such a coordination in an automatic way by using evolutionary methods. The architecture is completely neural network based and it allows the control of the whole robot for, in principle, any type of task based on sensory-motor coordination. The article shows how the proposed architecture is capable of controlling an Aibo robot for the performance of three different difficult tasks (standing, standing up and walking) using exactly the same neural distribution. It is also expected that it will be directly scalable for higher levels of control and general design in evolutionary robotics.